Machine learning as a service (MLaaS) is a set of cloud services that machine learning providers offer as a part of cloud computing services. MLaaS providers offer tools including face recognition, data visualization, application programming interface (APIs), predictive analytics, natural language processing, and deep learning. The main attraction of these services is that, like any other cloud service, users can get started with a machine learning system without the need to install software or provision of the servers. Infrastructural concerns like model training, data pre-processing, model evaluation, and ultimately, predictions, can be alleviated with the help of MLaaS.

Machine Learning (ML), globally recognized as a key driver of digital transformation, will be responsible for cumulative investments of $58 billion by the end of 2021. The global ML industry, growing at a CAGR of 42 percent, will be worth almost $9 billion in the latter part of 2022. The neural networks market will be worth over $23 billion in 2024.

Top Machine Learning-as-a-Service Providers

Microsoft Azure Machine Learning Studio

Microsoft Azure gloats scalable machine learning services for all sizes. Microsoft’s Azure machine learning studios are suitable for all artificial intelligence and data scientist beginners and experts. Azure supports a collection of frameworks, programming languages, databases, operating systems, and devices. It provides cross-device experience with support for all major mobile platforms.

AWS Machine Learning

AWS stands for Amazon Web Service. Amazon Machine Learning has a high level of automation that is useful for beginners. Without having to create the code, it helps businesses to build machine learning models. AWS makes machine learning obtainable to developers without learning complex machine learning algorithms and technology. The Amazon ML service is based on the pay-as-you-go pricing model.

IBM Watson Machine Learning

WML runs on IBM’s Bluemix. Both data scientists and developers use WML to be capable of training and scoring. WML is designed to answer the questions of operationalization, deployment, and deriving business values from ML models. WML also skits visual modeling tools that help users to gain understanding, make faster decisions, and quickly identify patterns.

Google Cloud Machine Learning Engine

Google’s scope of Software-as-a-Service is nearly endless. Google’s cloud machine learning engine is based on TensorFlow. This ML engine is integrated with all other Google services like Google Cloud Storage, Google Cloud Dataflow, Google BigQuery, among others. Google’s cloud machine learning engine provides users with a substitute for creating ML models for data. The data could be of any size and type.

BigML

BigML is flexible and easy to use deployment. In BigML’s web UI, there are many features integrated. BigML allows importing data from Microsoft Azure, Dropbox, Google Drive, Google Storage, AWS, etc. BigML has an extensive gallery of free models and datasets. Apart from this, BigML also has useful clustering algorithms and visualizations. With the help of the anomaly detection feature, it can detect pattern anomalies, which helps to save money and time.

Domino

Domino supports the latest data analysis workflow. It supports languages like R, Python, MATLAB, Julia, Perl, shell scripts, etc. Data science managers, data scientists, IT executives, and leaders use the Domino platform. Domino can smooth knowledge management with all the projects that are stored, and searchable.

HPE Haven On Demand

Using Haven machine learning solutions, businesses can analyze, extract, and index multiple data formats. These data could be audio, video, and email. Haven has approx 60 Application programming interface (APIs) available, that includes attributes like speech recognition, face detection, media analysis, image classification, object recognition, speech recognition, scene change detection, etc.

Arimo

Arimo can crunch massive amounts of data in seconds, using large computing platforms and machine learning algorithms. Arimo has the ability to predict future actions by learning from past behaviors. These predictions help with higher business outcomes. The service provider works upon time-series data to discover patterns of behavior, is based upon deep learning (DL).

Dataiku Data Science Studio

Dataiku supports programming languages like Python, R, Spark, Hive, Scala, Pig, etc. It provides machine learning solutions like MLlib, Scikit-Learn, H2O, Xgboost. To deliver, explore, build, and prototype data products efficiently, data scientists, engineers, and data analysts use this collaborative data science platform.

MLJAR

MLJAR provides its services for prototyping, development, and deploying a pattern recognition algorithm. Features of MLJAR are one interface for many algorithms, built-in hyper-parameters search, etc. To start working with MLJAR, a user first needs to upload the dataset, after selecting the dataset there is a need to select input and target attributes. After that, the machine learning service provider will automatically find the matching Machine learning algorithm.

Wrap Up

According to a study, the MLaaS market will witness a 49 percent growth during the forecast period 2017-2023, and over 20 billion units of equipment (excluding PCs, tablets, and smartphones) will form the IoT by 2020. (source). MLaaS helps companies enable better and quicker decision making by providing faster and invisible insights. MLaaS has the ability to integrate with different types of sensors as well.

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The boons of machine learning have been leveraged in the industry in the past many years. With its increasing implementation, the ML tools have also evolved with time. Today, people can easily work with machine learning owing to its easy-to-use, user-friendly tools. As the gathering of data and turning it into actionable insights has been automated enough, people with some knowledge of technology and motivation can work with ML.

These tools possess the strength to handle the mundane work of collecting data, adding structure and consistency where possible, and then starting the calculation. The modern-day tools can simplify the data gathering process and keeping that information in rows and columns.

Such user-friendly features are paving the way for people who work with numbers, spreadsheets and data towards machine learning while eliminating the need to be great at programming and data science.

Below are the five tools that simplify using machine learning algorithms.

Splunk

Splunk’s original version started off as a tool for searching through the voluminous log files created by modern web applications. Since then it has grown to analyze all forms of data, especially time-series and others produced in sequence. The latest newest versions of Splunk includes apps that integrate the data sources with machine learning tools like TensorFlow and some of the best Python open-source tools. Such modern tools offer quick solutions for detecting outliers, flagging anomalies and generating predictions for future values.

DataRobot

DataRobot incorporates a variety of regression techniques, ranging from the simplest (linear regression) to complicated statistical classic regression models, to more complex techniques including gradient boosting and neural networks. The platform can also solve simple binary classification problems, as well as highly complex multiclass classification problems with up to 100 different categories. Imagine being able to predict which product a customer is likely to purchase next, or why a customer is likely to churn, with a high degree of accuracy. With DataRobot it’s easy to automate the creation of machine learning models like this – with unprecedented transparency so you can understand and trust the predictions they make.

H2O

H2O has made it easy for non-experts to experiment with machine learning. In order for machine learning software to truly be accessible to non-experts, the company has designed an easy-to-use interface that automates the process of training a large selection of candidate models. H2O’s AutoML can also be a helpful tool for the advanced user, by providing a simple wrapper function that performs a large number of modeling-related tasks that would typically require many lines of code, and by freeing up their time to focus on other aspects of the data science pipeline tasks such as data-pre-processing, feature engineering and model deployment. It can be employed for automating the machine learning workflow, which includes automatic training and tuning of many models within a user-specified time-limit.

RapidMiner

RapidMiner’s automated machine learning can exponentially reduce the time and effort required to create predictive models for all businesses and organizations regardless of size, resources or industry. With its Auto Model, it’s possible to build predictive models in just 5 clicks. There’s no need for technical expertise. All users need to do is upload his data and specify the outcomes he wants, then Auto Model will produce the high-value insights he needs. RapidMiner Auto Model is part of a path to fully automated data science, from data exploration to modeling to production, when combined with Turbo Prep and Model Ops in RapidMiner Studio Enterprise.

BigML 

BigML’s AutoML is an Automated Machine Learning tool for BigML. The first version of AutoML helps automate the complete Machine Learning pipeline, not only the model selection. To boot, it’s pretty easy to execute. The user needs to give it training and validation datasets and it will give back a Fusion with the best possible models using the least possible number of features. BigML’s AutoML performs three main operations: Feature Generation, Feature Selection, and Model Selection.

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